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Found 7 papers, 2 papers with code
Testing the Robustness of Learned Index Structures
To simulate adversarial workloads, we carry out a data poisoning attack on linear regression models that manipulates the cumulative distribution function (CDF) on which the learned index model is trained.
Intelligent Autonomous Intersection Management
Connected Autonomous Vehicles will make autonomous intersection management a reality replacing traditional traffic signal control.
Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning
We then extend our architecture to learn heuristics for the real-time version of combinatorial optimization problems where all input features of a problem are not known a prior, but rather learned in real-time.
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees
Our comprehensive empirical evaluation against a state-of-the-art commercial tuning tool demonstrates up to 75% speed-up on shifting and ad-hoc workloads and up to 28% speed-up on static workloads in analytical processing environments.
DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees
Automating physical database design has remained a long-term interest in database research due to substantial performance gains afforded by optimised structures.
Dynamic Graph Configuration with Reinforcement Learning for Connected Autonomous Vehicle Trajectories
In this paper, we propose an efficient traffic optimization solution, called Coordinated Learning-based Lane Allocation (CLLA), which is suitable for dynamic configuration of lane-directions.
A Note on Bounding Regret of the C$^2$UCB Contextual Combinatorial Bandit
We revisit the proof by Qin et al. (2014) of bounded regret of the C$^2$UCB contextual combinatorial bandit.
